Author(s): 

Dirk Ifenthaler

Cooperation Partners:

Chair of Learning, Design and Technology

Description:

The field of learning analytics is generating growing interest in data and computer science as well educational science and hence, becoming an important aspect of modern e-learning environments. However, implementing learning analytics into existing legacy systems in learning environments with high data privacy concerns is quite a challenge. This research shows how a learning analytics application has been implemented into such an existing university environment by adding a plugin to the local digital learning environment which injects user centric information to specific objects within students’ and teachers’ learning environment. The LeAP platform can be used in various learning and teaching scenarios in order to provide adaptive support for learners and teachers.

Author(s): 

Leonie Gehrmann, Florian Stahl

Cooperation Partners: 

Hebrew University (Israel), Economist

Description:

We analyze how print advertising content has adapted to changes in speed-of-life, best defined as the “rapidity and density of experiences, meanings, perceptions, and activities” (Werner et al. 1985), over time.

On the one hand, the development of our image mining algorithm promises relevance for a variety of alternative use cases, since it is easily extendable to images beyond the print advertisements in our data set. Similarly, the analysis could be continued for other magazines, as well as social media postings, further refining our algorithm. On the other hand, our research also has direct implications for marketing practice. Increasing advertising clutter is a largely recognized phenomenon and our findings are likely to provide insights into the existing competition for consumer attention.

Author(s): 

Andreas Bayerl, Florian Stahl, Jacob Goldenberg

Cooperation Partners:

IDC Israel, Kununu.de

Description:

We analyze a data set of in total more than 10 Mio online reviews from two different platforms collecting User-Generated Content to find differences in review writing behavior across time-of-the-day, week day, season, weather conditions, a city’s life quality and other factors. Online reviews are meant to be an unbiased source of information. We will show how in fact, they can be influenced by externalities that have nothing or very little to do with the reviewed instance. By doing so we will follow a suggestion by Miller (2011) from his Science article saying that the power of the Internet and unstructured data can help take research to the next level. Methods employed during this project are among others, web scraping to acquire data, Natural Language Processing (NLP) to analyze data and Machine Learning to develop a model predicting review scores based on externalities. 

Author(s): 

Yasid Soufi, Florian Kraus

Cooperation Partners: 

Chair of Sales & Services Marketing

Description:

Gaining insights into predictors of a customer’s purchase behavior and personal traits enables a more flexible and adaptive approach to the sales process. Utilizing data on behavioral patterns, consumer characteristics and self-reported traits, we aim to understand and anticipate customer behavior in an app-based context. This is made possible by the application of machine learning algorithms to the enriched data set and leads to a better understanding of mechanisms underlying consumer decisions and the leveraging of predictive abilities in the optimization of the sales process.

Applications arise in many areas, in which behavioral patterns can be observed and more enriched customer data could possibly be gathered. This data, whilst valuable, remains often unsaved and unexplored. Insights may be valuable, both from a customer and provider perspective, as optimized processes lead to higher customer satisfaction and operational efficiency.

Author(s): 

Yasid Soufi, Florian Kraus

Cooperation Partners: 

Chair of Sales & Services Marketing

Description:

Skillful communication with participants of financial markets enables the development of new financing sources and ameliorated sentiment towards a company’s activities and value potential. Well-executed promotion in the context of Investor Relations can reduce opportunity costs when going public and afterwards drive stock returns as well as ease return volatility.

High-quality Investor Relations (IR) can be a critical success factor for corporations with high exposure to reception in the financial community. Firms aiming to undergo an Initial Public Offering (IPO) as well as firms, which are already listed, are legally bound to provide filings to authorities and the public, that can be utilized as a means of communication and promotion of a firm’s value potential.  Crawling large amounts of textual data and deploying methodologies of textual analysis, we aim to explore, how marketing should be communicating in the frame of Investor Relations.

Author(s): 

Prof. Oliver Spalt, Alberto Manconi, Antonino Rizzo

Cooperation Partners:

Chair of Financial Markets and Financial Institutions

Description:

Using a novel text-based measure of top management team diversity, covering over 70,000 top executives in over 6,500 U.S. firms from 1999 to 2014, we show that analyst forecasts are systematically more pessimistic for firms with more diverse top management teams ( diverse firms ), especially for inexperienced analysts. Institutional investors, especially if located in conservative areas, are less likely to hold diverse firms, even though diverse firms do not exhibit inferior returns. Consistent with downward-biased expectations, abnormal returns on information-release days are systematically positive for diverse firms. Combined, our results suggest stock markets are biased against diversity in top management teams.

Author(s): 

Elisa Casi, Katarzyna Bilicka, Carol Seregni, Barbara Stage, Christoph Spengel

Cooperation Partners:

Chair of Business Administration and Taxation II

Description:

In reponse to the debates about tax avoidance of multinational companies, legislators have introduced several tax transparency measures. With the insights of our study, we aim at providing policy advice on the desirability of mandating qualitative tax disclosure.

We study the effects of mandatory qualitative tax information disclosure on tax avoidance. We consider a tax transparency reform in UK that required firms to disclose tax strategy reports. We manually collect around 2,000 tax strategy reports and apply textual analysis techniques involving supervised machine learning tools.